Understanding the layered brain architecture for motivation: Dynamical systems, computational neuroscience, and robotic approaches
The Psychology of learning and motivation/The psychology of learning and motivation,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
Language: Английский
Personalizing Activity Selection in Assistive Social Robots from Explicit and Implicit User Feedback
International Journal of Social Robotics,
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 9, 2024
Abstract
Robots
in
multi-user
environments
require
adaptation
to
produce
personalized
interactions.
In
these
scenarios,
the
user’s
feedback
leads
robots
learn
from
experiences
and
use
this
knowledge
generate
adapted
activities
preferences.
However,
preferences
are
user-specific
may
suffer
variations,
so
learning
is
required
personalize
robot’s
actions
each
user.
can
obtain
Human–Robot
Interaction
by
asking
users
their
opinion
about
activity
(explicit
feedback)
or
estimating
it
interaction
(implicit
feedback).
This
paper
presents
a
Reinforcement
Learning
framework
for
social
selection
using
obtained
users.
also
studies
role
of
user
learning,
asks
whether
combining
explicit
implicit
produces
better
robot
adaptive
behavior
than
considering
them
separately.
We
evaluated
system
with
24
participants
long-term
experiment
where
they
were
divided
into
three
conditions:
(i)
adapting
that
was
how
much
liked
activities;
(ii)
metrics
generated
actions;
(iii)
feedback.
As
we
hypothesized,
results
show
both
values
when
correlating
initial
final
scores,
overcoming
individual
found
kind
does
not
affect
engagement
number
carried
out
during
experiment.
Language: Английский
Exploring the Effects of Multi-Factors on User Emotions in Scenarios of Interaction Errors in Human–Robot Interaction
Wa Gao,
No information about this author
Yuan Tian,
No information about this author
Shiyi Shen
No information about this author
et al.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(18), P. 8164 - 8164
Published: Sept. 11, 2024
Interaction
errors
are
hard
to
avoid
in
the
process
of
human–robot
interaction
(HRI).
User
emotions
toward
could
further
affect
user’s
attitudes
robots
and
experiences
HRI
so
on.
In
this
regard,
present
study
explores
effects
different
factors
on
user
when
occur
HRI.
There
is
sparse
research
directly
studying
perspective.
doing,
three
factors,
including
robot
feedback,
passive
active
contexts,
previous
emotions,
were
considered.
Two
stages
online
surveys
with
465
participants
implemented
explore
self-reporting
Then,
a
Yanshee
was
selected
as
experimental
platform,
61
recruited
for
real
empirical
based
two
surveys.
According
results
statistical
analysis,
we
conclude
some
design
guides
can
cope
scenarios
errors.
For
example,
feedback
have
impacts
after
encountering
errors,
but
contexts
do
not.
no
interactive
between
factors.
The
approach
reduce
negative
cases
HRI,
such
providing
irrelevant
on,
also
illustrated
contributions.
Language: Английский
Immune Algorithm in Automation Control of Intelligent Industrial Robots
Yan Guo
No information about this author
Lecture notes on data engineering and communications technologies,
Journal Year:
2024,
Volume and Issue:
unknown, P. 367 - 376
Published: Jan. 1, 2024
Language: Английский
Estímulos, pulsiones y ritmos biológicos como estrategias motivadoras del comportamiento de robots autónomos
Published: Aug. 28, 2023
La
robótica
social
ha
demostrado
en
los
últimos
años
un
gran
potencial
sectores
estratégicos
para
la
sociedad
como
educación,
sanidad
y
compañía
a
mayores.
Estos
necesitan
sistemas
autónomos
ayudar
sus
profesionales
por
diversos
motivos.
Por
ejemplo,
el
envejecimiento
de
población
países
desarrollados
provocado
una
falta
trabajadores
cualificados
clave
cuidado
personas
Este
artículo
tiene
motivación
desarrollo
robots
sociales
que
sean
capaces
realizar
tareas
adecuadamente
con
aceptación
usuarios,
reduzcan
supervisión
humana,
operen
escenarios
reales
forma
ininterrumpida.
trabajo
toma
referencia
numerosos
estudios
revelan
beneficios
replicar
comportamiento
humano
incrementar
vínculo
humano-robot
mejorar
interacción
entre
ambos.
Para
dotar
bioinspirado
autónomo
sociales,
este
proponemos
tres
estrategias
generación
comportamiento:
cómo
robot
percibe
evalúa
estímulos
del
entorno
responde
ellos,
pulsiones
internas
motivan
voluntario,
ritmos
biológicos
generar
comportamientos
cíclicos.
Estas
se
implementan
Mini,
permitiéndole
operar
durante
largos
periodos
tiempo
cumplir
asistencia
al
usuario.